Given the contextual information from two perspectives, i.e., global scene context and local group context, we need to find the right way to exploit these two contexts as well as the original re-ID features. To make full use of our context features, we design an embedding decomposition module in an implicit way and an explicit way, respectively. We first introduce our implicit design, as shown in Fig. REF . In the implicit way, we concatenate our two re-ID features and two context features, and then employ a channel attention block [1]} to give different weights to each feature. Afterward, we feed the single feature vector to the NAE layer.
The second design of this module is more concise and explicit. As shown in Fig. REF , our explicit design involves an NAE layer and a concatenation operation. As a result, the identity features of a target person consist of four parts: its original shallow and deep re-ID features, global context features which represent the scene, and local context features indicating the representations of its surrounding persons. Through the OIM [2]} loss, these two types of context features will also be a part of the representation of the target person.
